Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration
TL;DR: A critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration is presented, to provide various concepts utilized for extraction of the features to detect and classify the P Q disturbances even in the noisy environment.
Abstract: The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.
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TL;DR: In this article , the authors provide an up-to-date review of the most recent global trend of various renewable energy integrations into the power sector and discuss the role of RE integration in sustainable development.
55 citations
TL;DR: In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, which contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid.
Abstract: Connecting different renewable energy sources (RESs) to the electrical grids is presently being urged to fulfill the enormous need for electric power and to decrease traditional sources’ ecological related issues, the so-called hybrid systems. Unfortunately, these hybrid systems suffer from the possible negative environmental impacts of the wind gusts in wind energy conversion systems (WECSs) that may degrade the overall system performance. Additionally, various severe faults may disconnect some RESs from the hybrid system, like three-phase faults. In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid. The STATCOM is stimulated by two PI controllers regulating the reactive power flow between the STATCOM and the hybrid system at PCC and, consequently, regulating the voltage at PCC. A metaheuristic optimizer optimally schedules these two PI controllers based on whale optimization algorithm (WOA). The impartial comparison between the WOA dynamic performance and the particle swarm optimization as another optimization algorithm verifies the efficiency of the WOA for the near-optimal gain scheduling of the PI controller gains.
52 citations
TL;DR: In this paper, the authors present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons, with the viewpoint of the types of power input signal (synthetic/real/noisy), preprocessing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline).
Abstract: Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its pre-eminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.
37 citations
TL;DR: The proposed hybrid convolutional neural network method is a novel approach that covers the steps of an expert examining a signal and its classification performance is relatively high compared to other methods, the computational complexity is almost the same.
Abstract: As a result of the widespread use of power electronic equipment and the increase in consumption, the importance of effective energy policies and the smart grid begins to increase. Nonlinear loads and other loads in electric power systems are considered as the main reason for power quality disturbance. Distortions in signal quality and shape due to power quality disturbance cause a decrease in total efficiency. The proposed hybrid convolutional neural network method consists of a 1D convolutional neural network structure and a 2D convolutional neural network structure. The features acquired by these two convolutional neural network architectures are classified using the fully connected layer, which is traditionally used as the classifier of convolutional neural network architectures. Power signals are processed using a 1D convolutional neural network in their original form. Then these signals are converted into images and processed using a 2D convolutional neural network. Then, feature vectors generated by 1D and 2D convolutional neural networks are combined. Finally, this combined vector is classified by a fully connected layer. The proposed method is well suited to the nature of signal processing. It is a novel approach that covers the steps of an expert examining a signal. The proposed framework is compared with other state-of-the-art power quality disturbance classification methods in the literature. While the proposed method's classification performance is relatively high compared to other methods, the computational complexity is almost the same.
34 citations
27 Jul 2014
TL;DR: In this paper, the authors provided an improved power quality (PQ) disturbances classification, which were associated with load changes and environmental factors, by employing support vector machines (SVM) and decision tree classifiers.
Abstract: Penetration of distributed generation (DG) systems in conventional power systems leads to power quality (PQ) disturbances. This paper provides an improved PQ disturbances classification, which are associated with load changes and environmental factors. Various forms of PQ disturbances, including sag, swell, notch and harmonics, are taken into account. Several features are obtained through HS-transform, out of which optimal features are selected using a genetic algorithm (GA). These optimal features are used for PQ disturbances classification by employing support vector machines (SVM) and decision tree (DT) classifiers. The study is supported on three different case studies, considering experimental set-up prototypes for wind energy and photovoltaic (PV) systems, as well as the modified Nordic 32-bus test system. The robustness and precision of DT and SWM is performed with noise and harmonics in the disturbance signals, thus providing comprehensive results.
31 citations
References
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TL;DR: In this article, a grid simulator based on a back-to-back inverter topology with resonant controllers is presented to test the performance of a given system under such distorted voltages.
Abstract: In this study, a grid simulator based on a back-to-back inverter topology with resonant controllers is presented. The simulator is able to generate three-phase voltages for a range of amplitudes and frequencies with different types of perturbations, such as voltage sags, steady-state unbalanced voltages, low-order harmonics and flicker. The aim of this equipment is to test the performance of a given system under such distorted voltages. A prototype of the simulator, consisting of two inverters connected back-to-back to a 380 V three-phase grid and feeding a micro-grid composed of two-inverter interfaced distributed generators and a critical load was built and tested. A set of experimental results for linear purely resistive loads, non-linear loads and current-controlled inverters is presented to prove the capabilities of the simulator. Finally, a case study is presented by testing a micro-grid.
39 citations
TL;DR: An improved GART model (IGART) is proposed, and the experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.
Abstract: Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.
39 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...These includes, hardware and software architecture of expert system [197], rule-based model [198], improved generalized adaptive resonance theory (IGART) [199], recurrence quantification analysis [200], stochastic ordering theory with coded quickest classification [201], variety of supervised NN with online learning capabilities [202], attribute weighted artificial immune evolutionary classifier (AWAIEC) [203], spectral kurtosis to separate hybrid PQ disturbances [204], DT initialized fuzzy C-means clustering system based on ST [205], variational mode decomposition (VMD) [206], real-time calculation of the spectral kurtosis [207], online PQDs detection and classification using DWT, MM and SVD [208], curvelet transform and deep learning [209], rule-based ST and adaboost with decision stump as weak classifier [87], random forests based PQ assessment framework [82], deep learningbased method and stacked auto-encoder, as a deep learning framework [210], ICA with a sparse autoencoder (SAE) for gaining automatically training features [211] and a new classspecific weighted random vector functional link network (CSWRVFLN) [137]....
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...model [198], improved generalized adaptive resonance theory (IGART) [199], recurrence quantification analysis [200], stochastic ordering theory with coded quickest classification [201], variety of supervised NN with online learning capabilities [202], attribute weighted artificial immune evo-...
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TL;DR: The TT-transform variant is derived from the well known S-transform and employs a new window function whose width is inversely proportional to the frequency raised to a constant power with values within 0 and 1.
39 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...immune algorithm [176], least-square SVM based classifier to estimate the significant contingencies in a standard IEEE39 bus system in [177]....
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TL;DR: A new approach for power quality (PQ) event detection and classification is proposed based on an automatic four step algorithm that automatically classifies the PQ disturbances.
Abstract: In this paper a new approach for power quality (PQ) event detection and classification is proposed. This approach is based on an automatic four step algorithm. First the acquired voltage signals are represented in a 3-D space referential. Then principal component analysis is performed. In the third, features are extracted from the obtained eigenvalues of each disturbance waveforms. Finally a neuro-fuzzy based classifier automatically classifies the PQ disturbances. To show the effectiveness of the proposed method several case studies are presented. From the obtained results it is possible to confirm that the proposed approach can effectively classify different PQ disturbances.
38 citations
TL;DR: Comparisons show that the detection performance of WPTSE is better than that of wavelet transform and EEMD (ensemble empirical mode decomposition).
38 citations
"Comprehensive Review on Detection a..." refers methods in this paper
...tion techniques include, interpolated DFT [58], actual data based noise-suppression method usingWT and un-decimated WT [59], integrated rule-based approach of DWT-FFT [60], DTCWT and sparse presentation classifier (SRC) [61], combine wavelet packet and t-sallis entropy [62], empirical-WT based time-frequency technique [63], rank wavelet support...
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